2 (Strong) Exponential Time Hypothesis

2 (Strong) Exponential Time Hypothesis Version: 174 In this chapter, we will introduce one of the main hardness assumptions used in this course, the Strong Exponential Time Hypothesis. To this end, we first discuss current algorithms for Satisfiability. We then postulate increasingly strong assumptions about its time complexity: P 6= NP, the Exponential Time Hypothesis (ETH), and the Strong Exponential Time Hypothesis (SETH). To illustrate them, we give conditional lower bounds for the dominating set problem based on these assumptions. Finally, we show that SETH implies the OV hypothesis, making it a stronger assumption. Let us first introduce the satisfiability problem. Recall that a Boolean formula f is in conjunctive normal form (CNF) if it is a conjunction of clauses. A clause is a disjunction of literals, and each literal is either a (x1 _ x2 _ x3) ^ (x2 _ x3 _ x4) ^ (x2 _ x4) Boolean variable x or its negation x . i i Figure 2.1: Example of a CNF formula Problem 2.1. Let k 2 N. k-SAT Given: CNF formula f with: • N variables • clause width k • M clauses Determine: Is there a satisfying assignment for f? A satisfying assignment for f is an as- By Cook’s Theorem, we know that k-SAT is NP-hard for k ≥ 3. signment of true/false values to the vari- Thus, P 6= NP implies that there is no polynomial-time algorithm for ables such that all the clauses of f are satisfied, i.e, it contains x where x is set k-SAT for k ≥ 3. (For k = 2, we can solve the problem in polynomial i i to true or a negation xi where xi is set to time.) false. Note: There are at most (N)2k distinct Let us introduce our running example for this chapter: the domi- k clauses of width k. Thus, for fixed k 2 nating set problem. N, we typically assume without loss of N k k generality that M ≤ ( k )2 = O(N ); i.e., Problem 2.2. the input for k-SAT is of polynomial size in N. 2 fine-grained complexity theory Dominating Set (DomSet) Given: Undirected graph G = (V, E) with n vertices, integer q 2 N. Determine: Is there a dominating set S of size q? A dominating set S is a subset n A baseline algorithm solves Dominating Set in time O (q) · qn : of V such that each v 2 V is For each size-q subset of V, we check whether each v 2 V satisfies dominated by S, i.e., we either v 2 S or has an edge to some u 2 S. The latter test can be done in have v 2 S or there exists an n time O(q) per vertex v, and there are (q) size-q subsets of V, thus the edge fu, vg 2 E such that u 2 S. running time bound follows. Unfortunately, as q can be as large as Q(n), this gives no polynomial-time algorithm in the worst case. 2.1 Lower Bounds under P 6= NP Could there be a polynomial-time algorithm for Dominating Set? We give a negative answer, assuming P 6= NP. This gives a simple example for a huge number of lower bounds that were proven under the P 6= NP conjecture throughout decades of research since Karps’s list of 21 NP-complete problems. Theorem 2.3. Dominating Set cannot be solved in polynomial-time unless P = NP. Proof. We give a standard polynomial-time reduction from the NP- hard problem 3SAT to Dominating Set: 3-SAT Dominating Set • N variables time O(N + M) • n = 3N + M nodes −−−−−−−−! • M clauses • q = N Since the above reduction runs in polynomial time O(N + M), a polynomial-time algorithm for dominating set would allow us to de- cide any 3SAT instance in polynomial-time, which would contradict P 6= NP. Let us give a reduction as specified above. Let f be a 3-CNF formula. We construct a graph G as follows: For each variable xi, we introduce literal vertices xi, xi and a dummy vertex di. We connect these three vertices to form a triangle. Additionally, for each clause Cj, we introduce a clause vertex representing Cj. Finally, we connect a literal . ` C C ` . vertex to a clause vertex if and only if contains the literal , i.e., ... j j . C = (` _ `0 _ `00) for some literals `0, `00. Observe that G has 3N + M . vertices. ... Claim 2.4. G has a dominating set of size N if and only if f is satisfiable. Figure 2.2: Illustration of constructed graph G. (strong) exponential time hypothesis, version: 174 3 Proof. If there is a satisfying assignment for f, then let S be the set of literal vertices ` such that ` is true under this assignment. Note that S includes for any variable xi precisely one of xi and xi and thus jSj = N. We show that S is a dominating set. For each variable xi, the vertices xi, xi, di are dominated: one of xi and xi is in S, and the other two vertices are connected to this vertex. Furthermore, each clause vertex Cj is dominated: Our satisfying assignment sets some literal ` of Cj to true. Thus, the literal vertex `, which is contained in S, has an edge to the clause vertex for Cj, which is thus dominated by S. Conversely, let S be a dominating set in G of size N. To dominate all vertices xi, xi, di for i 2 [N], S must contain, for each i 2 [N], precisely one of xi, xi, di. We define an assignment for f, by setting each xi to true if xi 2 S, to false if xi 2 S and to an arbitrary value, say true, if di 2 S. We claim that this assignment satisfies f: Each clause vertex Cj must be dominated by some literal vertex ` in S, which by definition requires that Cj contains `. As our assignment is chosen such as to set every literal ` 2 S to true, Cj must be satisfied. The above claim proves correctness of the reduction. Furthermore, observe that the graph G can be constructed in time O(N + M). Con- sequently, if Dominating Set is solvable in polynomial time, then 3-SAT is solvable in polynomial time. 2.2 Lower Bounds under ETH Given that we do not expect polynomial-time algorithms for k-SAT and Dominating Set, what are the fastest (superpolynomial) algorithms for these problems? Let us first consider this question for 3-SAT. It is hardly surprising Notation: O∗(·) hides polynomial fac- that an extensive line of research has tried to obtain faster and faster tors. algorithms for this problem: 1 Burkhard Monien and Ewald Specken- 3-SAT Algorithms (partial list): meyer. Solving satisfiability in less than 2n steps. Discrete Applied Mathematics, 10 • O∗ 2N: trivial – check each of the 2N assignments in poly(N) time (3):287–295, 1985 2 O∗ N 1 Uwe Schöning. A probabilistic algo- • 1.6181 : Monien and Speckenmeyer rithm for k-SAT and constraint satisfac- ∗ • O 1.3334N : Schöning2, randomized tion problems. In FOCS’99, pages 410– • O∗ 1.3334N: Moser, Scheder3 414, 1999 3 Robin A. Moser and Dominik Scheder. • ... A full derandomization of Schöning’s k- ∗ • O 1.3280N : Liu4 SAT algorithm. In STOC’11, pages 245– • O∗ 1.3071N: PPSZ algorithm5 with modifications by Hertli6, ran- 252, 2011 4 Sixue Liu. Chain, generalization of cov- domized ering code, and deterministic algorithm for k-SAT. In ICALP’18, pages 88:1–88:13, A natural question is: When the research effort of the community 2018 5 Ramamohan Paturi, Pavel Pudlák, Michael E. Saks, and Francis Zane. An improved exponential-time algorithm for k-SAT. J. ACM, 52(3):337–364, 2005 6 Timon Hertli. 3-SAT faster and simpler - Unique-SAT bounds for PPSZ hold in general. SIAM J. Comput., 43(2):718–729, 2014 4 fine-grained complexity theory tends to infinity, what is the fastest algorithm that we will obtain? More formally, we define the following constant: Definition 2.5. For any k ≥ 3, we define dN sk := inffd j k-SAT has an O(2 )-time algorithmg. Intuitively, we would hope that if we continue our collective search for faster algorithms, eventually, we will find O(2sk·n+#)-time k-SAT algorithms for some very small # > 0. Currently, it is far from clear what the true value of, e.g., s3 should be. If P = NP, orp if 3-SAT has a subexponential algorithm (e.g., run- N ning in time O(2 )), then s3 = 0. The fastest algorithm in the list above gives the upper bound s3 < 0.3863. Given the importance of 3-SAT for theoretical computer science, and the extensive effort of algorithmic research on this problem, it is plau- sible to conjecture that in fact s3 > 0 – this is in essence the conjecture that 3-SAT has no subexponential-time algorithms. This conjecture is called the Exponential Time Hypothesis postulated by Impagliazzo and Paturi7. 7 Russell Impagliazzo and Ramamohan Paturi. On the complexity of k-SAT. Jour- Hypothesis 2.6 (Exponential Time Hypothesis (ETH)). The Exponential nal of Computer and System Sciences, 62(2): Time Hypothesis postulates that 367–375, 2001 s3 > 0. An equivalent formulation is the following: There is some d > 0 such that 3-SAT cannot be solved in time O(2dN). A note on randomness: In the definition of sk (and thus in the hypothesis above), we allow for both deterministic and randomized algorithms.

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